





"""simple docstring"""


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				List[str]										=  """facebook/bart-large-mnli"""
			__lowerCAmelCase     :				Union[str, Any]										=  (
			    """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
			    """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
			    """It returns the most likely label in the list of provided `labels` for the input text."""
			)
			__lowerCAmelCase     :				Dict										=  """text_classifier"""
			__lowerCAmelCase     :				Optional[int]										=  AutoTokenizer
			__lowerCAmelCase     :				Union[str, Any]										=  AutoModelForSequenceClassification

			__lowerCAmelCase     :				Any										=  ["""text""", ["""text"""]]
			__lowerCAmelCase     :				List[str]										=  ["""text"""]







			def   _a      (      self    ):






									'''simple docstring'''







									super().setup()
									UpperCamelCase				:		int				   =			self.model.config
									UpperCamelCase				:		List[str]				   =			-1
									for idx, label in config.idalabel.items():
															if label.lower().startswith("""entail"""    ):
																					UpperCamelCase				:		str				   =			int(_A    )
									if self.entailment_id == -1:
															raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init."""    )







			def   _a      (      self						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		int				   =			labels
									return self.pre_processor(
									    [text] * len(_A    )						,   [f"""This example is {label}""" for label in labels]						,   return_tensors="""pt"""						,   padding="""max_length"""						,   )







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			outputs.logits
									UpperCamelCase				:		Optional[int]				   =			torch.argmax(logits[:, 2]    ).item()
									return self._labels[label_id]









'''simple docstring'''


class  __lowercase    :  # Public class to implement a graph







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     None:
     __a								=       row
     __a								=       col
     __a								=       graph







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     bool:
     return (
         0 <= i < self.ROW
         and 0 <= j < self.COL
         and not visited[i][j]
         and self.graph[i][j]
     )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     None:
     # Checking all 8 elements surrounding nth element
     __a								=       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
     __a								=       [-1, 0, 1, -1, 1, -1, 0, 1]
     __a								=       True  # Make those cells visited
     for k in range(8   ):
       if self.is_safe(i + row_nbr[k]   ,				j + col_nbr[k]   ,				UpperCamelCase   ):
         self.diffs(i + row_nbr[k]   ,				j + col_nbr[k]   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     int:  # And finally, count all islands.
     __a								=       [[False for j in range(self.COL   )] for i in range(self.ROW   )]
     __a								=       0
     for i in range(self.ROW   ):
       for j in range(self.COL   ):
         if visited[i][j] is False and self.graph[i][j] == 1:
           self.diffs(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
           count += 1
     return count



